Topological Information, Flux Balance Analysis, and Extreme Pathways Extraction for Metabolic Networks Behaviour Investigation

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Abstract

In recent years biological processes modeling and simulation have become two key issues in analyzing complex cellular systems. Information about metabolic networks is often incomplete, since a large portion of available data is ignored by its probabilistic nature. The main objective of this work is to investigate metabolic networks behavior in terms of their fault tolerance capabilities as random removal of network nodes and high-connectivity-degree node deletion aimed at compromising or modifying network activity. This paper proposes a software framework, namely CEllDataLaB, containing three tasks to perform the structural and functional analysis: topological analysis, flux balance analysis and extreme pathway algorithm. The performed trials have shown that the node connectivity degrees as well as the node functional role in the network are key issues to evaluate the impact of node deletion on network behavior and activity. The metabolic network used in this work is related to the human hepatocyte metabolism.
Lingua originaleEnglish
Stato di pubblicazionePublished - 2011

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Fluxes
Functional analysis
Fault tolerance
Metabolism
Structural analysis
Metabolic Networks and Pathways
Hepatocytes

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence

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title = "Topological Information, Flux Balance Analysis, and Extreme Pathways Extraction for Metabolic Networks Behaviour Investigation",
abstract = "In recent years biological processes modeling and simulation have become two key issues in analyzing complex cellular systems. Information about metabolic networks is often incomplete, since a large portion of available data is ignored by its probabilistic nature. The main objective of this work is to investigate metabolic networks behavior in terms of their fault tolerance capabilities as random removal of network nodes and high-connectivity-degree node deletion aimed at compromising or modifying network activity. This paper proposes a software framework, namely CEllDataLaB, containing three tasks to perform the structural and functional analysis: topological analysis, flux balance analysis and extreme pathway algorithm. The performed trials have shown that the node connectivity degrees as well as the node functional role in the network are key issues to evaluate the impact of node deletion on network behavior and activity. The metabolic network used in this work is related to the human hepatocyte metabolism.",
author = "Filippo Sorbello and Salvatore Vitabile and Vincenzo Conti",
year = "2011",
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AU - Sorbello, Filippo

AU - Vitabile, Salvatore

AU - Conti, Vincenzo

PY - 2011

Y1 - 2011

N2 - In recent years biological processes modeling and simulation have become two key issues in analyzing complex cellular systems. Information about metabolic networks is often incomplete, since a large portion of available data is ignored by its probabilistic nature. The main objective of this work is to investigate metabolic networks behavior in terms of their fault tolerance capabilities as random removal of network nodes and high-connectivity-degree node deletion aimed at compromising or modifying network activity. This paper proposes a software framework, namely CEllDataLaB, containing three tasks to perform the structural and functional analysis: topological analysis, flux balance analysis and extreme pathway algorithm. The performed trials have shown that the node connectivity degrees as well as the node functional role in the network are key issues to evaluate the impact of node deletion on network behavior and activity. The metabolic network used in this work is related to the human hepatocyte metabolism.

AB - In recent years biological processes modeling and simulation have become two key issues in analyzing complex cellular systems. Information about metabolic networks is often incomplete, since a large portion of available data is ignored by its probabilistic nature. The main objective of this work is to investigate metabolic networks behavior in terms of their fault tolerance capabilities as random removal of network nodes and high-connectivity-degree node deletion aimed at compromising or modifying network activity. This paper proposes a software framework, namely CEllDataLaB, containing three tasks to perform the structural and functional analysis: topological analysis, flux balance analysis and extreme pathway algorithm. The performed trials have shown that the node connectivity degrees as well as the node functional role in the network are key issues to evaluate the impact of node deletion on network behavior and activity. The metabolic network used in this work is related to the human hepatocyte metabolism.

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